Learning Objectives: - Apply spatial operations to answer policy-relevant research questions - Integrate census demographic data with spatial analysis - Create publication-quality visualizations and maps - Work with spatial data from multiple sources - Communicate findings effectively for policy audiences
Part 1: Healthcare Access for Vulnerable Populations
Research Question
Which Pennsylvania counties have the highest proportion of vulnerable populations (elderly + low-income) living far from hospitals?
Your analysis should identify counties that should be priorities for healthcare investment and policy intervention.
Required Analysis Steps
Complete the following analysis, documenting each step with code and brief explanations:
Step 1: Data Collection (5 points)
Load the required spatial data: - Pennsylvania county boundaries - Pennsylvania hospitals (from lecture data) - Pennsylvania census tracts
Coordinate Reference System:
User input: NAD83
wkt:
GEOGCRS["NAD83",
DATUM["North American Datum 1983",
ELLIPSOID["GRS 1980",6378137,298.257222101,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["latitude",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["longitude",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4269]]
Questions to answer: - How many hospitals are in your dataset? 219 Hospitals - How many census tracts? 3445 Census Tracts - What coordinate reference system is each dataset in? pa_county_boundries = EPSG: 3857 pa_hospitals = EPSG: 4326 census_tracts = EPSG: 4269 —
Step 2: Get Demographic Data
Use tidycensus to download tract-level demographic data for Pennsylvania.
Required variables: - Total population - Median household income - Population 65 years and over (you may need to sum multiple age categories)
Your Task:
# Get demographic data from ACSpa_acs_data =get_acs(geography ="tract",state ="PA",variables =c(total_pop ="B01003_001",median_income ="B19013_001",male_65_66 ="B01001_020",male_67_69 ="B01001_021",male_70_74 ="B01001_022",male_75_79 ="B01001_023",male_80_84 ="B01001_024",male_over_85 ="B01001_025",female_65_66 ="B01001_044",female_67_69 ="B01001_045",female_70_74 ="B01001_046",female_75_79 ="B01001_047",female_80_84 ="B01001_048",female_over_85 ="B01001_049" ),year =2022,survey ="acs5",output ="wide")# Column for total 65+ populationpa_acs_data = pa_acs_data %>%mutate(over_65= male_65_66E + male_67_69E + male_70_74E + male_75_79E + male_80_84E + male_over_85E +female_65_66E + female_67_69E + female_70_74E + female_75_79E + female_80_84E + female_over_85E )# Answering the question asking about missing income data and median income across all tractsmissing = pa_acs_data %>%summarise(count_missing =sum(is.na(median_incomeE)))missing
# Join to tract boundariesacs_tracts = census_tracts %>%left_join(pa_acs_data, by ="GEOID")
Questions to answer: - What year of ACS data are you using? 2022 - How many tracts have missing income data? 63 - What is the median income across all PA census tracts? Im assuming this is asking for the mean across all tracts? that would be: $77527.23
Step 3: Define Vulnerable Populations
Identify census tracts with vulnerable populations based on TWO criteria: 1. Low median household income (choose an appropriate threshold) 2. Significant elderly population (choose an appropriate threshold)
Your Task:
# Filter for vulnerable tracts based on your criteria# First make income categoriesacs_tracts = acs_tracts %>%mutate(income_category =case_when( median_incomeE <50000~"Low", median_incomeE >=50000& median_incomeE <=60000~"Moderate", median_incomeE >60000~"High",TRUE~NA_character_ ) )# Create percentages for populations over 65acs_tracts = acs_tracts %>%mutate(pct_over_65 = (over_65 / total_popE) *100 )# I want to get the mean and median and range for pct_over_65 to decide what threshold to choose.acs_tracts %>%st_drop_geometry() %>%# added because of an errorsummarise(mean_pct_over_65 =mean(pct_over_65, na.rm =TRUE),median_pct_over_65 =median(pct_over_65, na.rm =TRUE),min_pct_over_65 =min(pct_over_65, na.rm =TRUE),max_pct_over_65 =max(pct_over_65, na.rm =TRUE) )
#Filter for both categoriesvulnerable_tracts = acs_tracts %>%filter( median_incomeE <50000, pct_over_65 >=25 )
Questions to answer: - What income threshold did you choose and why? I chose under $50000 to designate low median household income, because its measured by median not mean so there will be true population values in the tract above and below this number not represented by the median household income.
What elderly population threshold did you choose and why The mean and median are very close to 19%, the max is 100 and min is 0 so I will select above 25% as a significant elderly population.
How many tracts meet your vulnerability criteria? 78
What percentage of PA census tracts are considered vulnerable by your definition? 2.26%
Step 4: Calculate Distance to Hospitals
For each vulnerable tract, calculate the distance to the nearest hospital.
Your Task:
# Transform to appropriate projected CRSacs_tracts_2272 =st_transform(acs_tracts, crs =2272)pa_county_boundries_2272 =st_transform(pa_county_boundries, crs =2272)pa_hospitals_2272 =st_transform(pa_hospitals, crs =2272)vulnerable_tracts_2272 =st_transform(vulnerable_tracts, crs =2272)# Calculate distance from each tract centroid to nearest hospitalvulnerable_tract_centers =st_centroid(vulnerable_tracts_2272)hospital_distances =st_distance(vulnerable_tract_centers, pa_hospitals_2272)nearest_hospital =apply(hospital_distances, 1, min)nearest_hospital_miles = nearest_hospital /5280print(nearest_hospital_miles)
Requirements: - Use an appropriate projected coordinate system for Pennsylvania - Calculate distances in miles - Explain why you chose your projection - I choose EPSG 2272 because it works well for data in Philadelphia
Questions to answer: - What is the average distance to the nearest hospital for vulnerable tracts 2.17 Miles - What is the maximum distance? 10.97 Miles - How many vulnerable tracts are more than 15 miles from the nearest hospital? 0
Step 5: Identify Underserved Areas
Define “underserved” as vulnerable tracts that are more than 15 miles from the nearest hospital.
Your Task:
# Create underserved variableunderserved = vulnerable_tracts_2272_all[nearest_hospital_miles >15, ]# Even though I have no underserved tracts I will still add it to my data set vulnerable_tracts_2272_all = vulnerable_tracts_2272_all %>%mutate(underserved = nearest_hospital_miles >15)
Questions to answer: - How many tracts are underserved? 3 - What percentage of vulnerable tracts are undeserved? 3.8% - Does this surprise you? Why or why not? Yes, this is surprising, perhaps its due to the parameters I used to define vulnerable but, I would think that especially in rural tratcts in Pennsylvania that there would be a higher percentage of vulnerable people more than 15 miles from the nearest hospital. —
Step 6: Aggregate to County Level
Use spatial joins and aggregation to calculate county-level statistics about vulnerable populations and hospital access.
Your Task:
# Spatial join tracts to countiestracts_counties =st_join(vulnerable_tracts_2272_all, pa_county_boundries_2272)# Aggregate statistics by countystatistics_by_county <- tracts_counties %>%st_drop_geometry() %>%# drop geometrygroup_by(COUNTY_NAM) %>%# group by county namesummarise(vulnerable_tracts =n(), # number of vulnerable tractsunderserved_tracts =sum(underserved, na.rm =TRUE), # number of undeserved tractspct_underserved =100* underserved_tracts / vulnerable_tracts, # percentage of vulnerable tracts that are undeservedavg_distance_to_hospital_vulnerable =mean(nearest_hospital_miles, na.rm =TRUE), # average distance for vulnerable tractstotal_vulnerable_pop =sum(over_65, na.rm =TRUE) # total vulnerable population )statistics_by_county
Required county-level statistics: - Number of vulnerable tracts - Number of underserved tracts - Percentage of vulnerable tracts that are underserved - Average distance to nearest hospital for vulnerable tracts - Total vulnerable population
Questions to answer: - Which 5 counties have the highest percentage of underserved vulnerable tracts? CLARION, CLEARFIELD, JUNIATA, PERRY, FRANKLIN - Which counties have the most vulnerable people living far from hospitals? NORTHUMBERLAND, ALLEGHENY, CAMBRIA, LEHIGH - Are there any patterns in where undeserved counties are located? These are all rural counties located in central and south central PA
Step 7: Create Summary Table
Create a professional table showing the top 10 priority counties for healthcare investment.
Your Task:
# Create and format priority counties tablepriority_counties <-c("ALLEGHENY", "CAMBRIA", "FRANKLIN", "MERCER", "CLARION","PERRY", "LEHIGH", "NORTHUMBERLAND", "CLEARFIELD", "JUNIATA")# Filter for these counties and format numberstop_10_counties = statistics_by_county %>%filter(COUNTY_NAM %in% priority_counties) %>%mutate(vulnerable_tracts = (vulnerable_tracts),avg_distance_to_hospital_vulnerable =round(avg_distance_to_hospital_vulnerable, 2),total_vulnerable_pop = scales::comma(total_vulnerable_pop) ) %>%arrange(desc(vulnerable_tracts)) # sort by number of vulnerable tracts# Create tabletop_10_counties %>%select( COUNTY_NAM, vulnerable_tracts, avg_distance_to_hospital_vulnerable, total_vulnerable_pop ) %>%kable(caption ="Top 10 Priority Counties for Healthcare Investment",col.names =c("County","Vulnerable Tracts","Avg Distance to Hospital (mi)","Total Vulnerable Population" ),format ="html" )
Top 10 Priority Counties for Healthcare Investment
County
Vulnerable Tracts
Avg Distance to Hospital (mi)
Total Vulnerable Population
ALLEGHENY
16
2.81
9,854
CAMBRIA
5
5.13
3,324
MERCER
3
2.18
3,147
CLEARFIELD
2
11.11
1,830
FRANKLIN
2
8.57
1,624
NORTHUMBERLAND
2
10.55
1,149
CLARION
1
18.13
802
JUNIATA
1
15.91
492
LEHIGH
1
3.41
1,412
PERRY
1
15.91
492
Requirements: - Use knitr::kable() or similar for formatting - Include descriptive column names - Format numbers appropriately (commas for population, percentages, etc.) - Add an informative caption - Sort by priority (you decide the metric)
Part 2: Comprehensive Visualization
Using the skills from Week 3 (Data Visualization), create publication-quality maps and charts.
Map 1: County-Level Choropleth
Create a choropleth map showing healthcare access challenges at the county level.
Your Task:
# Create county-level access map#First bring geometry back from before I made county statistics tablestatistics_by_county_sf = statistics_by_county %>%left_join(select(tracts_counties, COUNTY_NAM, geometry), # only need COUNTY_NAM + geometryby ="COUNTY_NAM" ) %>%st_as_sf() # convert back to sfggplot() +geom_sf(data = pa_county_boundries_2272, fill ="NA", color ="black") +geom_sf(data = statistics_by_county_sf, aes(fill = pct_underserved), color ="white") +geom_sf(data = pa_hospitals_2272, shape =21, fill ="red", size = .25) +scale_fill_continuous(labels = scales::percent_format(accuracy =1)) +# formated labelslabs(title ="Healthcare Access Challenges At Pennslyvania County Level",subtitle ="Fill = Percentage of vulnerable tracts that are underserved", ) +theme_void()
Requirements: - Fill counties by percentage of vulnerable tracts that are undeserved - Include hospital locations as points - Use an appropriate color scheme - Include clear title, subtitle, and caption - Use theme_void() or similar clean theme - Add a legend with formatted labels
Map 2: Detailed Vulnerability Map
Create a map highlighting undeserved vulnerable tracts.
Your Task:
# Create detailed tract-level mapggplot() +# All vulnerable tracts in graygeom_sf(data = vulnerable_tracts_2272_all, fill ="lightgray", color =NA) +# Undeserved tracts highlighted in Purplegeom_sf(data =subset(vulnerable_tracts_2272_all, underserved ==TRUE),fill ="purple",color =NA ) +geom_sf(data = pa_county_boundries_2272, fill ="NA", color ="black", size =0.5) +geom_sf(data = pa_hospitals_2272, shape =21, fill ="black", size =1) +labs(title ="Underserved Vulnerable Tracts in Pennsylvania",subtitle ="Highlighted tracts = underserved tracts,counties and hospitals shown for context" ) +theme_void()
Requirements: - Show undeserved vulnerable tracts in a contrasting color - Include county boundaries for context - Show hospital locations - Use appropriate visual hierarchy (what should stand out?) - Include informative title and subtitle
Chart: Distribution Analysis
Create a visualization showing the distribution of distances to hospitals for vulnerable populations.
Your Task:
# Create distribution visualizationggplot(vulnerable_tracts_2272_all, aes(x = total_popE, y = nearest_hospital_miles)) +geom_point(alpha =0.6, color ="darkgreen", size =2) +labs(title ="Distance to Hospital vs. Vulnerable Population Size",x ="Vulnerable Population at Tract Level",y ="Distance to Nearest Hospital in miles" ) +theme_classic()
Suggested chart types: - Histogram or density plot of distances - Box plot comparing distances across regions - Bar chart of underserved tracts by county - Scatter plot of distance vs. vulnerable population size
Requirements: - Clear axes labels with units - Appropriate title - Professional formatting - Brief interpretation (1-2 sentences as a caption or in text)
Part 3: Bring Your Own Data Analysis
Choose your own additional spatial dataset and conduct a supplementary analysis.
# Load your additional datasetneighborhoods =st_read("data/philadelphia-neighborhoods.geojson")
Reading layer `philadelphia-neighborhoods' from data source
`/Users/JoshuaRigsby 1/Documents/MUSA/MUSA5080/Portfolio/portfolio-setup-jrigsbyr5/labs/lab2/data/philadelphia-neighborhoods.geojson'
using driver `GeoJSON'
Simple feature collection with 159 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -75.28026 ymin: 39.86701 xmax: -74.95576 ymax: 40.13799
Geodetic CRS: WGS 84
Questions to answer: - What dataset did you choose and why? I chose neighborhood boundries, public art, landmarks, and historic sites to identify neighborhoods that have better access to cultural amenities and neighborhoods that lack cultural amenities. - What is the data source and date? Source for all data sets: OpenDataPhilly - https://opendataphilly.org/datasets/, public art: July 2, 2020, neighborhood boundaries: July 2, 2020, landmarks: 11/01/2017, historic sites: N/A
- How many features does it contain? neighborhoods = 159, public art = 239, landmarks = 1147, historic sites = 23375 - What CRS is it in? Did you need to transform it? They are all in EPSG 4326, no need to transform.
Pose a research question
What neighborhoods in Philadelphia lack access and proximity to cultural amenities?
Examples: - “Do vulnerable tracts have adequate public transit access to hospitals?” - “Are EMS stations appropriately located near vulnerable populations?” - “Do areas with low vehicle access have worse hospital access?”
Conduct spatial analysis
Use at least TWO spatial operations to answer your research question.
# Your spatial analysis# I had to add this because the public art data has alot of incorrect ploygons,# but after trouble shooting it was still causing an error on line 672, and I still # wanted to include it in my analysis.sf::sf_use_s2(FALSE)# Spatial Join# First, join each amenity to neighborhoods separately this gave me 3 new spatial data frames.public_art_neighborhood =st_join(public_art, neighborhoods)historic_sites_neighborhhod =st_join(historic_sites, neighborhoods)landmarks_neighborhhod =st_join(landmarks, neighborhoods)# Count per neighborhood for each amenity - Point-in-polygon aggregation# Public artpublic_art_counts <- public_art_neighborhood %>%st_set_geometry(NULL) %>%group_by(NAME) %>%# Use NAME column from public_artsummarise(public_art_count =n())# Historic siteshistoric_counts <- historic_sites_neighborhhod %>%st_set_geometry(NULL) %>%group_by(NAME) %>%# Use NAME column from historic_sitessummarise(historic_count =n())# Landmarkslandmark_counts <- landmarks_neighborhhod %>%st_set_geometry(NULL) %>%group_by(NAME.y) %>%# Use NAME.y column from landmarkssummarise(landmark_count =n())# Make the counts into one data setneighborhood_counts = neighborhoods %>%st_set_geometry(NULL) %>%select(NEIGHBORHOOD = NAME) %>%left_join(public_art_counts, by =c("NEIGHBORHOOD"="NAME")) %>%left_join(historic_counts, by =c("NEIGHBORHOOD"="NAME")) %>%left_join(landmark_counts, by =c("NEIGHBORHOOD"="NAME.y")) %>%mutate(public_art_count =replace_na(public_art_count, 0),historic_count =replace_na(historic_count, 0),landmark_count =replace_na(landmark_count, 0) )# Kable with countskable( neighborhood_counts,col.names =c("Neighborhood", "Public Art", "Historic Sites", "Landmarks"),caption ="Cultural Assets per Neighborhood")
Cultural Assets per Neighborhood
Neighborhood
Public Art
Historic Sites
Landmarks
BRIDESBURG
0
0
4
BUSTLETON
0
4
2
CEDARBROOK
0
1
1
CHESTNUT_HILL
3
96
37
EAST_FALLS
1
229
9
MOUNT_AIRY_EAST
0
51
2
GRAYS_FERRY
0
2
8
OLNEY
0
6
5
PENNYPACK_PARK
0
4
14
SOMERTON
0
1
1
MOUNT_AIRY_WEST
0
104
4
WEST_OAK_LANE
0
0
1
WISSAHICKON_PARK
0
11
39
RIVERFRONT
2
316
27
BYBERRY
0
1
5
WEST_TORRESDALE
0
0
0
MECHANICSVILLE
0
0
0
PARKWOOD_MANOR
0
3
3
FRANKLIN_MILLS
0
0
0
NORTHEAST_AIRPORT
0
0
0
MODENA
0
0
1
MORRELL_PARK
0
0
1
MILLBROOK
0
0
4
CRESTMONT_FARMS
0
4
1
ACADEMY_GARDENS
0
0
0
PENNYPACK
0
1
1
ASTON_WOODBRIDGE
0
0
2
PENNYPACK_WOODS
0
21
2
WINCHESTER_PARK
0
0
2
TORRESDALE
0
4
9
LEXINGTON_PARK
0
0
1
RHAWNHURST
0
1
3
FOX_CHASE
0
7
9
BURHOLME
0
2
2
OXFORD_CIRCLE
0
1
3
SUMMERDALE
0
0
1
CRESCENTVILLE
0
0
1
LAWNDALE
1
5
2
NORTHWOOD
0
23
3
HOLMESBURG
0
7
7
FRANKFORD
0
11
4
MAYFAIR
0
0
3
TACONY
0
1
5
WISSINOMING
0
10
2
ANDORRA
0
0
6
UPPER_ROXBOROUGH
0
3
17
DEARNLEY_PARK
0
6
6
ROXBOROUGH_PARK
0
3
0
GERMANY_HILL
0
2
2
WISSAHICKON_HILLS
0
0
0
ROXBOROUGH
0
7
3
MANAYUNK
0
449
7
WISSAHICKON
0
27
0
GERMANTOWN_EAST
0
38
2
GERMANTOWN_MORTON
2
12
0
GERMANTOWN_WEST_CENT
0
46
3
GERMANTOWN_PENN_KNOX
3
142
3
GERMANTOWN_WESTSIDE
0
4
0
GERMANTOWN_SOUTHWEST
0
10
0
OGONTZ
0
1
10
WISTER
1
65
2
NICETOWN
1
41
0
TIOGA
0
1
3
ALLEGHENY_WEST
0
4
6
GLENWOOD
0
0
1
FERN_ROCK
0
0
3
EAST_OAK_LANE
0
0
1
MELROSE_PARK_GARDENS
0
0
1
FRANKLINVILLE
0
2
1
FELTONVILLE
0
0
1
RICHMOND
0
0
8
PORT_RICHMOND
0
0
1
HUNTING_PARK
0
2
5
JUNIATA_PARK
0
21
3
HARROWGATE
0
0
4
FAIRHILL
0
0
0
UPPER_KENSINGTON
0
2
2
MCGUIRE
0
0
1
STANTON
1
99
2
BREWERYTOWN
1
2
2
SHARSWOOD
0
1
4
NORTH_CENTRAL
4
189
4
YORKTOWN
4
0
6
LUDLOW
1
1
1
HARTRANFT
10
14
29
WEST_KENSINGTON
1
7
5
FISHTOWN
0
47
8
OLD_KENSINGTON
0
10
4
NORTHERN_LIBERTIES
7
38
7
LOGAN
0
33
13
SOCIETY_HILL
33
3472
11
OLD_CITY
22
3117
27
CHINATOWN
0
211
1
CENTER_CITY
4
177
12
WASHINGTON_SQUARE
21
1659
15
FAIRMOUNT
0
147
2
FRANCISVILLE
2
30
2
SPRING_GARDEN
2
2083
3
LOGAN_SQUARE
15
505
66
RITTENHOUSE
2
5995
21
FITLER_SQUARE
0
289
5
GRADUATE_HOSPITAL
1
1123
5
POINT_BREEZE
0
3
3
HAWTHORNE
1
10
1
BELLA_VISTA
0
23
5
QUEEN_VILLAGE
0
747
4
DICKINSON_NARROWS
0
6
0
PASSYUNK_SQUARE
1
0
4
GREENWICH
0
0
1
LOWER_MOYAMENSING
0
5
1
WHITMAN
0
0
1
INDUSTRIAL
6
0
8
AIRPORT
3
1
22
CLEARVIEW
0
1
0
PENROSE
3
0
2
PASCHALL
0
4
3
BARTRAM_VILLAGE
0
1
4
KINGSESSING
0
10
3
COBBS_CREEK
0
0
2
WALNUT_HILL
0
2
1
CEDAR_PARK
0
55
1
GARDEN_COURT
0
0
1
WOODLAND_TERRACE
0
48
3
SPRUCE_HILL
0
61
2
SOUTHWEST_SCHUYLKILL
0
1
2
UNIVERSITY_CITY
33
83
132
POWELTON
0
57
3
WEST_POWELTON
1
3
7
EAST_PARKSIDE
0
110
1
BELMONT
0
2
1
HAVERFORD_NORTH
0
1
0
WEST_PARKSIDE
0
0
1
MILL_CREEK
0
2
2
DUNLAP
0
0
1
MANTUA
4
24
15
HADDINGTON
4
1
1
CARROLL_PARK
0
0
3
OVERBROOK
0
469
18
WYNNEFIELD_HEIGHTS
0
0
3
CALLOWHILL
6
8
3
WEST_POPLAR
4
6
3
EAST_POPLAR
6
8
2
STRAWBERRY_MANSION
0
6
4
EAST_PARK
0
32
100
WYNNEFIELD
0
41
1
WEST_PARK
3
13
85
NORMANDY_VILLAGE
0
0
1
STADIUM_DISTRICT
0
0
49
NAVY_YARD
0
0
6
EAST_KENSINGTON
0
9
7
ELMWOOD
0
1
1
GIRARD_ESTATES
0
505
3
EASTWICK
19
0
7
PACKER_PARK
0
2
6
PENNSPORT
1
13
2
NEWBOLD
0
1
1
WEST_PASSYUNK
0
0
1
EAST_PASSYUNK
0
0
0
BLUE_BELL_HILL
0
8
6
# Join counts back to neighborhood data for visualizationneighborhoods_with_counts <- neighborhoods %>%left_join(neighborhood_counts, by =c("NAME"="NEIGHBORHOOD"))# Map# Create composite countneighborhoods_with_counts$amenity_total <- neighborhoods_with_counts$public_art_count + neighborhoods_with_counts$historic_count + neighborhoods_with_counts$landmark_count# Create mapggplot(neighborhoods_with_counts) +geom_sf(aes(fill = amenity_total), color ="black", size =0.2) +scale_fill_gradient(low ="#c6dbef", # darker light bluehigh ="#08519c", na.value ="white" ) +labs(fill ="Total Amenities",title ="Total Cultural Amenities per Neighborhood",subtitle ="Sum of Public Art, Historic Sites, and Landmarks" ) +theme_minimal() +theme(plot.title =element_text(face ="bold", size =16),plot.subtitle =element_text(size =12) )
# Make interactive with leafletlibrary(leaflet)leaflet(neighborhoods_with_counts) %>%addTiles() %>%# base mapaddPolygons(fillColor =~colorNumeric("Blues", amenity_total)(amenity_total),weight =1,color ="black",fillOpacity =0.7,popup =~paste0("<strong>", NAME, "</strong><br>Total Amenities: ", amenity_total) ) %>%addLegend(pal =colorNumeric("Blues", neighborhoods_with_counts$amenity_total),values =~amenity_total,title ="Total Amenities",position ="bottomright" )
Analysis requirements: - Clear code comments explaining each step - Appropriate CRS transformations - Summary statistics or counts - At least one map showing your findings - Brief interpretation of results (3-5 sentences)
Your interpretation: Upon looking at the interactive map it is evident that the Rittenhouse neighborhood has the greatest access to cultural amenities. My initial question was which neighborhoods lacked exposure and access to cultural amenities but upon doing the analysis it is evident that, nearly all of the neighborhoods in Philadelphia have a relatively low amount of cultural features, compared to neighborhoods in the city center. Objectively there may be many cultural features per neighborhood compared to other large cities in pennslyvania, but the only comparison we have here is within Philadelphia. The neighborhoods in Philadelphia following Rittenhouse with the greatest access to cultural amenities are: Old City, Society Hill, Spring Garden, Washington Square, Logan square and Graduate Hospital. It is not surprising that this concentration exists in and near the city center.
Finally - A few comments about your incorporation of feedback!
Take a few moments to clean up your markdown document and then write a line or two or three about how you may have incorporated feedback that you recieved after your first assignment. I made sure to remove any brackets and instruction text for the final submission, and I was more careful about duplicate columns when filtering and joining.
Submission Requirements
What to submit:
Rendered HTML document posted to your course portfolio with all code, outputs, maps, and text
Use embed-resources: true in YAML so it’s a single file
All code should run without errors
All maps and charts should display correctly
File naming:LastName_FirstName_Assignment2.html and LastName_FirstName_Assignment2.qmd
